Kumar Chandar S

Dr S. Kumar Chandar is working as an Associate Professor in School of Business & Management – CHRIST (Deemed to be University), Bangalore having 20 years in wide variety of technical, managerial and teaching roles. He completed PhD in the field of Artificial Neural Networks & PhD in Marketing Management & submitted DSc in Computer Science (Computational Finance). He has published and presented over 40 research papers (15 Scopus Indexed & 5 Web of Science Indexed) in International and National Journals in the area of Computer Science and Management. He is an AIMA certified Accredited Management Teacher in the Area of Information Technology, SMFI certified Strategic Management Teacher and completed ITIL V3 Foundation-level certificate in Service Management. He is actively involved in various professional bodies like NHRD, CSI, ISTE, AIMA and ACS. His areas of interest include Business Intelligence, Knowledge Management, Strategic Management, Artificial Intelligence, Machine Learning, Cognitive Science, Soft Computing, Natural Computing, Computational Finance, Text Analytics in Finance and Marketing, Quantum Computing in Finance and Algorithmic Trading.

EDUCATION

Master of Computer Applications
Master of Business Administration
PhD (Computer Science - Artificial Intelligence)
PhD (Management)

RESEARCH INTERESTS

Strategic Management
Artificial Intelligence
Computational Finance
Text Analytics in Finance
HR Analytics
Marketing Analytics
Innovation & Creativity
Soft Computing
Natural Computing

Scopus Publications

Climate Change and Rainfall Variability in Goa: A Hybrid LSTM-Autoencoder based Predictive Approach
Kumar Chandar S
Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026
A Machine Learning Approach to Crude Oil Price Prediction Using Support Vector Machine (SVM)
Hitesh Punjabi, S. Kumar Chandar, Mayur Malik
Lecture Notes in Networks and Systems, 2026
Enhancing Crude Oil Price Prediction with Neural Network Models
Hitesh Punjabi, S. Kumar Chandar, Mayur Malik
Lecture Notes in Networks and Systems, 2026
Predicting Crude Oil Futures using Feed Forward Neural Networks and Technical Indicators: A Comparative Study on WTI and Brent
Kumar Chandar S, Hitesh Punjabi, Mayur Malik
Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026
Intelligent Analytical Framework to Improve Customer Retention in the SaaS Industry
V. Sasikiran Angara, K. S. Manu, S. Kumar Chandar, Lakshmi Shankar Iyer
Smart Innovation Systems and Technologies, 2026
A Spiking Neural Network Approach to Electroencephalography based Consumer Preference Modeling
Kumar Chandar S., Vijayadurai J., Palanivel Rajan M.
Journal of Innovative Image Processing, 2025
Neuromarketing is an emerging interdisciplinary field that applies neuropsychology in marketing to study consumer sensory-motor actions such as cognitive and affective responses to marketing stimuli through Brain Computer Interface (BCI) technology. While marketers spend over 750 billion dollars annually on traditional marketing procedures such as surveys, interviews, and consumer’s feedback, these methods are often criticized for their inability to capture genuine consumer preferences. Neuromarketing promises to overcome such issues by analyzing neural responses directly. This paper presents a novel framework for predicting consumer preferences by analyzing Electroencephalography (EEG) signals. EEG signals are acquired from 25 volunteers while administering 14 products with three different variations. The EEG signals are preprocessed using Modified Wavelet Thresholding (MWT) to remove noise while preserving neural activity patterns. A third-generation network, Spiking Neural Network (SNN) is designed to recognize consumer preferences based on EEG frequency bands. Unlike conventional models, SNN captures temporal dynamics through spike timing, which is crucial for EEG signals. The efficacy of the model is tested across individual EEG bands to identify the most influential frequency band in decision-making. Simulation outcomes demonstrate that the proposed model can effectively predict consumer preferences. The model achieved an accuracy of 90.91%, recall of 90.7%, a precision of 91.14%, a specificity of 91.12%, and an F1-score of 90.92%. The outcomes highlight the potential of EEG based neuromarketing systems to decode subconscious consumer responses, enabling brands and businesses to design more targeted marketing strategies based on objective neural data.
Consumer Decision Recognition Based on EEG Signals for Neuromarketing Applications
S. Kumar Chandar, J. Vijayadurai, M. Palanivel Rajan
International Journal of Information Technology and Decision Making, 2025
Neuromarketing is a blooming interdisciplinary field that tries to understand the biology of consumer behavior by combining neuroscience with marketing. This technique can be used to grasp consumers’ hidden choices, intentions and decisions by analyzing their physiological and brain signals. Electroencephalography (EEG) is one of the popular neuroimaging techniques to capture and record the neural activity of the brain. Numerous research projections have been made in this field to achieve better results. Earlier approaches did not prioritize effective EEG signal preprocessing and classification methods. This paper presents a model to recognize consumer preferences by analyzing and classifying EEG signals. In this model, EEG signals are decomposed into many subbands using wavelet transform. An enhanced wavelet thresholding method is proposed to eliminate noise from subbands. Several wavelet features are computed from each subband and then fed as input to classifiers. Finally, three different machine learning classifiers are used to classify the signal between like and dislike. The classifiers are K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). EEG signals from 25 people are collected to verify the developed system’s performance. The effectiveness of the developed method with different classifiers is validated by varying brain lobe features and band features. In comparison to other classifiers like KNN and MLP, the designed system with the SVM classifier performs better and achieves an accuracy of 98.21%. The experimental findings for the developed system suggest that research in this area has the potential to alter and enhance marketing tactics for the benefit of both manufacturers and consumers, ultimately leading to a mutually beneficial outcome.
Electricity Demand Prediction: An Analytical Comparison of ARIMA and Artificial Neural Network
Kumar Chandar S
6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025
Electricity plays a dominant role globally, especially in the economies of India. Accurately projecting its consumption is crucial for energy planning. This study focuses on forecasting electricity consumption across distinct sectors using Autoregressive Integrate Moving Average (ARIMA) and Artificial Neural Network (ANN). The efficacy of the models is evaluated via various error metrics and compared, demonstrating the superior performance of the ANN model over ARIMA model.
A Fuzzy AHP Approach to Evaluation of Value Addition in the Indian Medical Equipment Supply Chain
P. Leslie Dass, Sreerengan V. R. Nair, Georgy P. Kurien, S. Kumar Chandar
Lecture Notes in Networks and Systems, 2025
A Multicriteria Decision-Making Approach to Building Resilience Along the Indian Medical Equipment Supply Chain
P. Leslie Dass, Sreerengan V. R. Nair, S. Kumar Chandar, Georgy P. Kurien
Smart Innovation Systems and Technologies, 2025
A systematic literature network analysis approach to assess the topology of modern-era supply chain risk management research
P. Leslie Dass, Sreerengan V.R. Nair, Georgy P. Kurien, S. Kumar Chandar
International Journal of Industrial and Systems Engineering, 2025
Impact of Restrictive Trade Policies, Channel Conflict and Uncertain Business Environment on Marketing Channel Satisfaction in FMCG Sector
Journal of Distribution Science, 2025
Cross Domain Lexicon Transfer -A Case Specific to Application in Banking Domain
Nagendra BV, Kumar Chandar S, J B Simha, Manu K S, Kirubanand K V, et al.
6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025
Retraction Note: Grey Wolf optimization-Elman neural network model for stock price prediction (Soft Computing, (2021), 25, 1, (649-658), 10.1007/s00500-020-05174-2)
S. Kumar Chandar
Soft Computing, 2024
Generation of Dynamic Table Using Magic Square to Enhance the Security for the ASCII CODE Using RSA
N. G. Pooja, K. Mani, Uma Devi, S. Kumar Chandar
SN Computer Science, 2024
Deep learning framework for stock price prediction using long short-term memory
S. Kumar Chandar
Soft Computing, 2024
Predicting Stock Market Movements Through Multisource Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
John Ranjith, Kumar Chandar S
International Journal of Engineering Trends and Technology, 2024
Real-time human action prediction using pose estimation with attention-based LSTM network
A. Bharathi, Rigved Sanku, M. Sridevi, S. Manusubramanian, S. Kumar Chandar
Signal Image and Video Processing, 2024
Transforming workplace stress: The importance of neuroleadership for building resilient work environment
Sebin Joy, Arti Arun Kumar, Sridevi Nair, Kumar S. Chandar
Neuroleadership Development and Effective Communication in Modern Business, 2024
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Kumar Kumar, Kumar Chandar S
Fusion Practice and Applications, 2024
Enhancing Small and Medium OEMs' Adoption of IIoT Technologies
Prassana Despande, S Kumar Chandar, N Elangovan
Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
Industry Internet of Things (IIoT) Adoption Pressures in SME OEMs
Prassana Despande, S Kumar Chandar, N Elangovan
Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence
B V Nagendra, S Kumar Chandar, J B Simha, J A John Bazil
Proceedings 2024 5th International Conference on Image Processing and Capsule Networks Icipcn 2024, 2024
Change in Outlook of Indian Industrial OEMs Towards IIoT Adoption During COVID-19
Prasanna V. Rao Deshpande, S. Kumar Chandar
Lecture Notes in Networks and Systems, 2023
Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm
Nagendra Bv, J B Simha, Shinu Abhi, Kumar Chandar S
7th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2023 Proceedings, 2023

RECENT SCHOLAR PUBLICATIONS

Intelligent Analytical Framework to Improve Customer Retention in the SaaS Industry
VS Angara, KS Manu, SK Chandar
Business Intelligence and Data Analytics: Proceedings of BIDA 2025, Volume 2 … , 2026
2026
Enhancing Crude Oil Price Prediction
H Punjabi¹, SK Chandar, M Malik
Trends in Sustainable Computing and Machine Intelligence: Proceedings of … , 2026
2026
Citations: 1
Consumer Decision Recognition Based on EEG Signals for Neuromarketing Applications
S Kumar Chandar, J Vijayadurai, M Palanivel Rajan
International Journal of Information Technology & Decision Making 24 (06 … , 2025
2025
A Multicriteria Decision-Making Approach to Building Resilience Along the Indian Medical Equipment Supply
PL Dass, SVR Nair, SK Chandar, GP Kurien
Business Intelligence and Data Analytics: Proceedings of BIDA 2024, 159 , 2025
2025
A systematic literature network analysis approach to assess the topology of modern-era supply chain risk management research
PL Dass, SVR Nair, GP Kurien, SK Chandar
International Journal of Industrial and Systems Engineering 50 (1), 106-145 , 2025
2025
Citations: 1
Retraction Note: Grey Wolf optimization-Elman neural network model for stock price prediction
SK Chandar
Soft Computing 28 (Suppl 2), 815-815 , 2024
2024
Generation of Dynamic Table Using Magic Square to Enhance the Security for the ASCII CODE Using RSA
NG Pooja, K Mani, U Devi, SK Chandar
SN Computer Science 5 (7), 906 , 2024
2024
A Fuzzy AHP Approach to Evaluation of Value Addition in the Indian Medical Equipment Supply Chain
PL Dass, SVR Nair, GP Kurien, SK Chandar
Congress on Intelligent Systems, 43-60 , 2024
2024
Deep learning framework for stock price prediction using long short-term memory
SK Chandar
Soft Computing 28 (17), 10557-10567 , 2024
2024
Citations: 11
Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence
BV Nagendra, SK Chandar, JB Simha, JAJ Bazil
2024 5th International Conference on Image Processing and Capsule Networks … , 2024
2024
Citations: 1
Real-time human action prediction using pose estimation with attention-based LSTM network
A Bharathi, R Sanku, M Sridevi, S Manusubramanian, SK Chandar
Signal, Image and Video Processing 18 (4), 3255-3264 , 2024
2024
Citations: 21
Predicting Stock Market Movements Through Multi-source Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
SKC John Ranjith
International Journal of Engineering Trends and Technology 72 (6), 8 , 2024
2024
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
KCS John Ranjith
Fusion: Practice and Applications (FPA) 16 (02), 178 , 2024
2024
Citations: 5
Change in Outlook of Indian Industrial OEMs Towards IIoT Adoption During COVID-19
PV Rao Deshpande, S Kumar Chandar
Inventive Computation and Information Technologies: Proceedings of ICICIT … , 2023
2023
Color image segmentation based on improved sine cosine optimization algorithm
S Mookiah, K Parasuraman, S Kumar Chandar
Soft Computing 26 (23), 13193-13203 , 2022
2022
Citations: 16
Convolutional neural network for stock trading using technical indicators
SK Chandar
Automated Software Engineering 29 (1), 16 , 2022
2022
Citations: 94
Cat swarm optimization algorithm tuned multilayer perceptron for stock price prediction
KS Chandar, H Punjabi
International Journal of Web-Based Learning and Teaching Technologies … , 2021
2021
Citations: 9
A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement Using Twitter Data and News Sentiments
SK Chandar, H Punjabi, MK Sharda, J Murugadhas
Data Science and Data Analytics: Opportunities and Challenges, 305 , 2021
2021
Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms
K Chandar S
Pattern Recognition Letters 147 (1), 124-133 , 2021
2021
Citations: 57
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
SK Chandar
International Journal of Networking and Virtual Organisations 25 (1), 29-47 , 2021
2021
Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

RETRACTED ARTICLE: Grey Wolf optimization-Elman neural network model for stock price prediction: S. Kumar Chandar
S Kumar Chandar
Soft Computing 25 (1), 649-658 , 2021
2021
Citations: 106
Convolutional neural network for stock trading using technical indicators
SK Chandar
Automated Software Engineering 29 (1), 16 , 2022
2022
Citations: 94
Prediction of stock market price using hybrid of wavelet transform and artificial neural network
SK Chandar, M Sumathi, SN Sivanandam
Indian journal of Science and Technology 9 (8), 1-5 , 2016
2016
Citations: 80
Forecasting Gold Prices Based on Extreme Learning Machine
SNS S. Kumar Chandar, M. Sumathi
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 11 (3), 9 , 2016
2016
Citations: 74
Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction
S Kumar Chandar
Journal of Ambient Intelligence and Humanized Computing, 1-9 , 2019
2019
Citations: 67
Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms
K Chandar S
Pattern Recognition Letters 147 (1), 124-133 , 2021
2021
Citations: 57
Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach
SK Chandar
Cluster Computing 22 (Suppl 6), 13159-13166 , 2019
2019
Citations: 44
Forecasting of foreign currency exchange rate using neural network
SK Chandar, M Sumathi, SN Sivanandam
International Journal of Engineering and Technology 7 (1), 99-108 , 2015
2015
Citations: 27
Real-time human action prediction using pose estimation with attention-based LSTM network
A Bharathi, R Sanku, M Sridevi, S Manusubramanian, SK Chandar
Signal, Image and Video Processing 18 (4), 3255-3264 , 2024
2024
Citations: 21
Color image segmentation based on improved sine cosine optimization algorithm
S Mookiah, K Parasuraman, S Kumar Chandar
Soft Computing 26 (23), 13193-13203 , 2022
2022
Citations: 16
Deep learning framework for stock price prediction using long short-term memory
SK Chandar
Soft Computing 28 (17), 10557-10567 , 2024
2024
Citations: 11
Stock price prediction based on technical indicators with soft computing models
S Kumar Chandar
International Conference on Image Processing and Capsule Networks, 685-699 , 2020
2020
Citations: 10
Soft computing and bio inspired computing techniques for stock market prediction–A comprehensive survey
SK Chandar
Int. J. Eng. Technol 7 (3), 1836-1845 , 2018
2018
Citations: 10
Cat swarm optimization algorithm tuned multilayer perceptron for stock price prediction
KS Chandar, H Punjabi
International Journal of Web-Based Learning and Teaching Technologies … , 2021
2021
Citations: 9
Foreign exchange rate forecasting using Levenberg-Marquardt learning algorithm
SK Chandar, M Sumathi, SN Sivanandam
Indian Journal of Science and Technology 9 (8), 1-5 , 2016
2016
Citations: 7
Neural network based forecasting of foreign currency exchange rates
SK Chandar, M Sumathi, SN Sivanandam
International Journal on Computer Science and Engineering 6 (6), 202 , 2014
2014
Citations: 6
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
KCS John Ranjith
Fusion: Practice and Applications (FPA) 16 (02), 178 , 2024
2024
Citations: 5
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
SK Chandar
International Journal of Networking and Virtual Organisations 25 (1), 29-47 , 2021
2021
Citations: 5
A study on forecasting mutual fund net asset value using neural network approach
IJ Rani, SK Chandar
Int J Future Revol Comput Sci Commun Eng 4 (3), 89-93 , 2018
2018
Citations: 4
Crude oil prediction using a hybrid radial basis function network
SK CHANDAR, M Sumathi, S Sivanandam
Journal of Theoretical and Applied Information Technology 72 (2) , 2015
2015
Citations: 4